CN108332758A - A kind of corridor recognition method and device of mobile robot - Google Patents
A kind of corridor recognition method and device of mobile robot Download PDFInfo
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- CN108332758A CN108332758A CN201810077081.3A CN201810077081A CN108332758A CN 108332758 A CN108332758 A CN 108332758A CN 201810077081 A CN201810077081 A CN 201810077081A CN 108332758 A CN108332758 A CN 108332758A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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- Automation & Control Theory (AREA)
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Abstract
The embodiment of the invention discloses a kind of corridor recognition method and devices of mobile robot.Wherein, this method includes：The first posture information according to odometer testing result prediction mobile robot；The laser data that the mobile robot acquires is matched by proximal method ICP algorithm with existing map based on iteration, obtains the second posture information of the mobile robot；Calculate the difference of first posture information and second posture information；If newer expected pose model convergence in advance, current posture information is selected according to the expected pose model and the difference from first posture information and second posture information；Map is updated according to the current posture information of selection and the laser data of acquisition.Technical solution provided in an embodiment of the present invention solves the problems, such as that mobile robot carries out positioning based on laser radar and builds the corridor in figure, so that built map is more accurate.
Description
Technical field
The present invention relates to technologies of intelligent mobile field more particularly to a kind of corridor recognition methods of mobile robot
And device.
Background technology
In the information age, with the development of intelligent mobile robot and universal, positioning is carried out based on laser radar and builds figure
Technology is also widely used due to its higher precision and without carrying out any modification to environment.
Currently, (iteration is with regard near point by the mobile ICP used in a gallery based on laser radar for intelligent mobile robot
Method, Iterative Closest Point) algorithm carry out positioning build figure, due to acquisition corridor image have similitude so that
ICP method is susceptible to misjudgment phenomenon when judging whether robot occurs mobile, i.e. ICP method judges that robot does not move, but
Actually robot may have occurred movement.
Invention content
The embodiment of the present invention provides a kind of corridor recognition method and device of mobile robot, according to advance newer expectation
Pose model updates map come the result that the output result or odometer that judge using ICP algorithm export, and solves shifting
Mobile robot carries out the corridor problem that positioning is built in figure based on laser radar, so that built map is more accurate.
In a first aspect, an embodiment of the present invention provides a kind of corridor recognition method of mobile robot, this method includes：
The first posture information according to odometer testing result prediction mobile robot；
The laser data acquired the mobile robot with regard to proximal method ICP algorithm based on iteration and existing map are carried out
Match, obtains the second posture information of the mobile robot；
Calculate the difference of first posture information and second posture information；
If newer expected pose model convergence in advance, according to the expected pose model and the difference from described
Current posture information is selected in first posture information and second posture information；
Map is updated according to the current posture information of selection and the laser data of acquisition.
Second aspect, the embodiment of the present invention additionally provide a kind of corridor recognition device of mobile robot, which includes：
First pose prediction module, for the first posture information according to odometer testing result prediction mobile robot；
Second pose acquisition module, for being swashed what the mobile robot acquired with regard to proximal method ICP algorithm based on iteration
Light data is matched with existing map, obtains the second posture information of the mobile robot；
Difference calculating module, the difference for calculating first posture information and second posture information；
Current pose acquisition module, if for newer expected pose model convergence in advance, according to the expected pose
Model and the difference select current posture information from first posture information and second posture information；
Map rejuvenation module, the laser data for current posture information and acquisition according to selection update map.
The corridor recognition method and device of mobile robot provided in an embodiment of the present invention will predict moving machine by odometer
The first posture information that device people obtains makes the difference to obtain difference, foundation with the second posture information of the robot of ICP algorithm output
The convergence and the difference of advance newer expected pose model select current posture information i.e. according to the advance newer phase
Pose model is hoped to judge that the result of output result or the odometer output using ICP algorithm updates map, is solved
Mobile robot carries out the corridor problem that positioning is built in figure based on laser radar, so that built map is more accurate.
Description of the drawings
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, of the invention other
Feature, objects and advantages will become more apparent upon：
Fig. 1 is a kind of flow chart of the corridor recognition method of the mobile robot provided in the embodiment of the present invention one；
Fig. 2 is a kind of flow chart of the corridor recognition method of the mobile robot provided in the embodiment of the present invention two；
Fig. 3 is a kind of flow chart of the corridor recognition method of the mobile robot provided in the embodiment of the present invention three；
Fig. 4 is a kind of structure diagram of the corridor recognition device of the mobile robot provided in the embodiment of the present invention four.
Specific implementation mode
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is only used for explaining the present invention rather than limitation of the invention.It also should be noted that for the ease of
Description, only some but not all contents related to the present invention are shown in the drawings.
Embodiment one
Fig. 1 is a kind of flow chart of the corridor recognition method for mobile robot that the embodiment of the present invention one provides, this implementation
Example is applicable to intelligent mobile robot and is being carried out the case where map is built in positioning in corridor based on laser radar.This method can be with
It is executed by the corridor recognition device of mobile robot provided in an embodiment of the present invention, which can be used software and/or hardware
Mode realize.Referring to Fig. 1, this method specifically includes：
S110, the first posture information according to odometer testing result prediction mobile robot.
Wherein, posture information refers to position and the posture of mobile robot, and position indicates mobile robot with respect to world coordinates
Position (translation), generally use coordinate (x, y) indicate, posture indicate mobile robot practical advance side of yaw angle, that is, robot
Misalignment angle between desired direction of advance can be selected Φ and indicate.It is three dimensions letter that posture information is corresponding as a result,
Breath can be used (x, y, Φ) to indicate.Corresponding first posture information can use n1=(x1, y1, Φ 1) to indicate.
Odometer is a kind of for measuring stroke and the device of speed, is provided with odometer in mobile robot, can be used for
Estimate posture information i.e. the first posture information of mobile robot.
S120 is carried out with regard to the laser data that proximal method ICP algorithm acquires mobile robot with existing map based on iteration
Matching, obtains the second posture information of mobile robot.
Wherein, iteration is used to seek matching relationship between point set with regard to proximal method ICP algorithm, solution the result is that two point sets
Between translation and rotation amount.Laser data is to pass through smothing filtering mistake by the laser radar acquisition in mobile robot
Filter the data after noise.
Specifically, using the laser data of mobile robot acquisition as the first point set, has corresponding data in map and make
For the second point set, concentrate each point a pair of with second point centrostigma one after three dimensions converts by first point in three dimensions
It answers, the translation and rotation amount between two solved point are the second posture information of mobile robot.Correspondingly, second
Appearance information can use n2=(x2, y2, Φ 2).
S130 calculates the difference of the first posture information and the second posture information.
First posture information n1 of the mobile robot that odometer obtains is subtracted into the mobile robot that ICP algorithm obtains
First posture information n2 can be obtained difference n3, n3=(x3, y3, Φ 3).
S140, if newer expected pose model convergence in advance, according to expected pose model and difference from first
Current posture information is selected in appearance information and the second posture information.
Wherein, expected pose model is the three-dimensional Gaussian that the difference based on the first posture information and the second posture information is established
Model, the member in model are known as the covariance matrix of three-dimensional mean vector and three-dimensional mean vector；Three-dimensional mean vector is
One posture information vector corresponding with the mean value of the difference of the second posture information；Covariance matrix is 3 X, 3 matrix.Judge three-dimensional
The convergent condition of Gauss model is whether the mark value of covariance matrix is sufficiently small, and the mark value of covariance refers to all leading diagonals
On the sum of element.Element value under initialization condition in expected pose model is 0, corresponding newer expected pose in advance
Model is will to be obtained after the input of the difference of the first posture information and the second posture information.
When the mark value of covariance matrix in advance newer expected pose model is less than preset threshold value such as less than 10-6
When, then newer expected pose model convergence in advance is can determine, at this point, can be according to certain rule from the first posture information and the
Select a posture information as current posture information in two posture informations.It such as can be true according to expected pose model and difference
Odometer and ICP algorithm which precision higher determined, so that it is determined that using the first posture information or the second posture information as current
Posture information.
Illustratively, can also include：If advance newer expected pose model is not restrained, the second posture information is selected
Make current posture information.
It should be noted that under normal circumstances, odometer cumulative errors are big, as mobile robot wheel slip when, ICP
The precision of the data of algorithm output is relatively high.It therefore, can be by the second pose when advance newer expected pose model is not restrained
The posture information of the mobile robot of information, that is, ICP algorithm output is elected to be current posture information, while updating mileage predicted value.
S150 updates map according to the current posture information of selection and the laser data of acquisition.
Specifically, according to the current posture information of obtained mobile robot, the laser data that laser radar acquires is inserted
Enter into existing map corresponding position and updates the map.
The corridor recognition method of mobile robot provided in an embodiment of the present invention will predict that mobile robot obtains by odometer
To the second posture information of robot of the first posture information and ICP algorithm output make the difference to obtain difference, according in advance more
The convergence and the difference of new expected pose model select current posture information i.e. according to advance newer expected pose
Model updates map come the result that the output result or odometer that judge using ICP algorithm export, and solves moving machine
Device people carries out the corridor problem that positioning is built in figure based on laser radar, so that built map is more accurate.
Embodiment two
Fig. 2 is a kind of corridor recognition method flow diagram of mobile robot provided by Embodiment 2 of the present invention.The present embodiment
Based on the embodiment of the present invention one, further provides and a kind of believe from the first pose according to expected pose model and difference
The method that current posture information is selected in breath and the second posture information.Referring to Fig. 2, this method specifically includes：
S210, the first posture information according to odometer testing result prediction mobile robot.
S220 is carried out with regard to the laser data that proximal method ICP algorithm acquires mobile robot with existing map based on iteration
Matching, obtains the second posture information of mobile robot.
S230 calculates the difference of the first posture information and the second posture information.
S240, if newer expected pose model convergence in advance, calculating difference and the mean value in expected pose model to
Mahalanobis distance between amount.
Wherein, it is that the first posture information is believed with the second pose that the mean vector in expected pose model, which is three-dimensional mean vector,
The corresponding vector of mean value of the difference of breath；Mahalanobis distance indicates the covariance distance of data, and being that one kind is effective calculates two not
Know the method for the similarity of sample set.
Therefore, when newer expected pose model convergence in advance, by calculating by the first posture information in the present embodiment
Mahalanobis distance between the difference obtained with the second posture information and three-dimensional mean vector, to select the present bit of mobile robot
Appearance information.
First posture information is elected to be current posture information by S250 if mahalanobis distance is more than distance threshold；Otherwise, will
Second posture information is elected to be current posture information.
Wherein, distance threshold is pre-set, can be modified according to actual demand；Specifically, mahalanobis distance is smaller
That is for mahalanobis distance in threshold range, the expected pose model of foundation is better, then the posture information of ICP algorithm output is more accurate can
It selects it as current posture information, while the posture information that odometer is predicted being updated；Otherwise, mahalanobis distance is bigger,
The expected pose model of foundation more deviates true situation, then the posture information that odometer is predicted is selected to believe as current pose
Breath, while according to the posture information of odometer result update mobile robot.
Illustratively, it after selecting current posture information in the first posture information and the second posture information, can also wrap
It includes according to obtained difference update expected pose model.Specific operation process can be：It is equal according to obtained difference update difference
Value, and update the mean vector in expected pose model according to newer difference mean value；According to newer mean vector regeneration period
Hope the covariance matrix in pose model.
While due to determining current pose according to mahalanobis distance, also in the posture information of update odometer prediction, therefore,
The difference for the posture information that the posture information of odometer prediction is exported with ICP algorithm can also change, and expected pose model
It is to be established based on difference, so while difference variation, corresponding expected pose model also needs to update, i.e., foundation obtains
Difference is by updating element mean vector and covariance matrix update update expected pose model in expected pose model.
S260 updates map according to the current posture information of selection and the laser data of acquisition.
The corridor recognition method of mobile robot provided in an embodiment of the present invention will predict that mobile robot obtains by odometer
To the second posture information of robot of the first posture information and ICP algorithm output make the difference to obtain difference, according in advance more
The convergence and the difference of new expected pose model select current posture information i.e. according to advance newer expected pose
Model updates map come the result that the output result or odometer that judge using ICP algorithm export, and solves moving machine
Device people carries out the corridor problem that positioning is built in figure based on laser radar, so that built map is more accurate.
Embodiment three
Fig. 3 is a kind of corridor recognition method flow diagram of mobile robot provided by Embodiment 2 of the present invention.The present embodiment
Based on above-described embodiment, a kind of preferable example is provided.Referring to Fig. 3, this method specifically includes：
S310, the first posture information according to odometer testing result prediction mobile robot.
S320 is carried out with regard to the laser data that proximal method ICP algorithm acquires mobile robot with existing map based on iteration
Matching, obtains the second posture information of mobile robot.
S330 calculates the difference of the first posture information and the second posture information.
S340 judges whether newer expected pose model convergence restrains in advance, if convergence, thens follow the steps S350；It is no
Then, step S370 is executed.
S350, the mahalanobis distance between mean vector in calculating difference and expected pose model.
First posture information is elected to be current posture information by S360 if mahalanobis distance is more than distance threshold；Otherwise, will
Second posture information is elected to be current posture information.
Second posture information is elected to be current posture information by S370.
S380 updates map according to the current posture information of selection and the laser data of acquisition.
The corridor recognition method of mobile robot provided in an embodiment of the present invention will predict that mobile robot obtains by odometer
To the second posture information of robot of the first posture information and ICP algorithm output make the difference to obtain difference, according in advance more
The convergence and the difference of new expected pose model select current posture information i.e. according to advance newer expected pose
Model updates map come the result that the output result or odometer that judge using ICP algorithm export, and solves moving machine
Device people carries out the corridor problem that positioning is built in figure based on laser radar, so that built map is more accurate.
Example IV
Fig. 4 is a kind of structure diagram of the corridor recognition device for mobile robot that the embodiment of the present invention four provides, the dress
The corridor recognition method that can perform the mobile robot that any embodiment of the present invention is provided is set, has the corresponding work(of execution method
It can module and advantageous effect.As shown in figure 4, the device may include：
First pose prediction module 410, for the first pose letter according to odometer testing result prediction mobile robot
Breath；
Second pose acquisition module 420, the laser for being acquired mobile robot with regard to proximal method ICP algorithm based on iteration
Data are matched with existing map, obtain the second posture information of mobile robot；
Difference calculating module 430, the difference for calculating the first posture information and the second posture information；
Current pose acquisition module 440, if for newer expected pose model convergence in advance, according to expected pose mould
Type and difference select current posture information from the first posture information and the second posture information；
Map rejuvenation module 450, the laser data for current posture information and acquisition according to selection update map.
The corridor recognition device of mobile robot provided in an embodiment of the present invention will predict that mobile robot obtains by odometer
To the second posture information of robot of the first posture information and ICP algorithm output make the difference to obtain difference, according in advance more
The convergence and the difference of new expected pose model select current posture information i.e. according to advance newer expected pose
Model updates map come the result that the output result or odometer that judge using ICP algorithm export, and solves moving machine
Device people carries out the corridor problem that positioning is built in figure based on laser radar, so that built map is more accurate.
Optionally, current pose acquisition module 440 specifically can be used for：
The mahalanobis distance between mean vector in calculating difference and expected pose model；
If mahalanobis distance is more than distance threshold, the first posture information is elected to be current posture information；Otherwise, by second
Appearance information is elected to be current posture information.
Optionally, current pose acquisition module also can be also used for 440：If advance newer expected pose model is not received
It holds back, then the second posture information is elected to be current posture information.
Illustratively, above-mentioned apparatus can also include：
Model modification module, for after selecting current posture information in the first posture information and the second posture information,
According to obtained difference update expected pose model.
Optionally, model modification module specifically can be used for：
According to obtained difference update difference mean value, and according to equal in newer difference mean value update expected pose model
Value vector；
The covariance matrix in expected pose model is updated according to newer mean vector.
Note that above are only presently preferred embodiments of the present invention and institute's application technology principle.It will be appreciated by those skilled in the art that
The present invention is not limited to specific embodiments described here, can carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out to the present invention by above example
It is described in further detail, but the present invention is not limited only to above example, without departing from the inventive concept, also
May include other more equivalent embodiments, and the scope of the present invention is determined by scope of the appended claims.
Claims (10)
1. a kind of corridor recognition method of mobile robot, which is characterized in that including：
The first posture information according to odometer testing result prediction mobile robot；
Proximal method ICP algorithm is matched the laser data that the mobile robot acquires with existing map based on iteration,
Obtain the second posture information of the mobile robot；
Calculate the difference of first posture information and second posture information；
If newer expected pose model convergence in advance, according to the expected pose model and the difference from described first
Current posture information is selected in posture information and second posture information；
Map is updated according to the current posture information of selection and the laser data of acquisition.
2. according to the method described in claim 1, it is characterized in that, according to the expected pose model and the difference from institute
It states in the first posture information and second posture information and selects current posture information, including：
Calculate the mahalanobis distance between the mean vector in the difference and the expected pose model；
If the mahalanobis distance is more than distance threshold, first posture information is elected to be current posture information；Otherwise, by institute
It states the second posture information and is elected to be current posture information.
3. according to the method described in claim 1, it is characterized in that, further including：
If advance newer expected pose model is not restrained, second posture information is elected to be current posture information.
4. according to the method described in claim 2, it is characterized in that, from first posture information and second posture information
After the middle current posture information of selection, further include：
The expected pose model is updated according to obtained difference.
5. according to the method described in claim 4, it is characterized in that, update the expected pose model according to obtained difference,
Including：
According to obtained difference update difference mean value, and update according to newer difference mean value equal in the expected pose model
Value vector；
The covariance matrix in the expected pose model is updated according to newer mean vector.
6. a kind of corridor recognition device of mobile robot, which is characterized in that including：
First pose prediction module, for the first posture information according to odometer testing result prediction mobile robot；
Second pose acquisition module, the laser number for being acquired the mobile robot with regard to proximal method ICP algorithm based on iteration
It is matched according to existing map, obtains the second posture information of the mobile robot；
Difference calculating module, the difference for calculating first posture information and second posture information；
Current pose acquisition module, if for newer expected pose model convergence in advance, according to the expected pose model
And the difference selects current posture information from first posture information and second posture information；
Map rejuvenation module, the laser data for current posture information and acquisition according to selection update map.
7. device according to claim 6, which is characterized in that the current pose acquisition module is specifically used for：
Calculate the mahalanobis distance between the mean vector in the difference and the expected pose model；
If the mahalanobis distance is more than distance threshold, first posture information is elected to be current posture information；Otherwise, by institute
It states the second posture information and is elected to be current posture information.
8. device according to claim 6, which is characterized in that the current pose acquisition module is additionally operable to：
If advance newer expected pose model is not restrained, second posture information is elected to be current posture information.
9. device according to claim 7, which is characterized in that further include：
Model modification module, for selected from first posture information and second posture information current posture information it
Afterwards, the expected pose model is updated according to obtained difference.
10. device according to claim 9, which is characterized in that the model modification module is specifically used for：
According to obtained difference update difference mean value, and update according to newer difference mean value equal in the expected pose model
Value vector；
The covariance matrix in the expected pose model is updated according to newer mean vector.
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|---|---|---|---|---|
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6349249B1 (en) * | 1998-04-24 | 2002-02-19 | Inco Limited | Automated guided apparatus suitable for toping applications |
| CN103150728A (en) * | 2013-03-04 | 2013-06-12 | 北京邮电大学 | Vision positioning method in dynamic environment |
| CN105953798A (en) * | 2016-04-19 | 2016-09-21 | 深圳市神州云海智能科技有限公司 | Determination method and apparatus for poses of mobile robot |
| CN106052676A (en) * | 2016-05-26 | 2016-10-26 | 深圳市神州云海智能科技有限公司 | Robot navigation positioning method and device and robot |
| CN106197427A (en) * | 2016-07-04 | 2016-12-07 | 上海思依暄机器人科技股份有限公司 | Method, device and the robot of a kind of indoor positioning navigation |
| CN106525053A (en) * | 2016-12-28 | 2017-03-22 | 清研华宇智能机器人（天津）有限责任公司 | Indoor positioning method for mobile robot based on multi-sensor fusion |
| WO2017131334A1 (en) * | 2016-01-25 | 2017-08-03 | 충북대학교 산학협력단 | System and method for recognizing location of mobile robot and making map |
-
2018
- 2018-01-26 CN CN201810077081.3A patent/CN108332758B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6349249B1 (en) * | 1998-04-24 | 2002-02-19 | Inco Limited | Automated guided apparatus suitable for toping applications |
| CN103150728A (en) * | 2013-03-04 | 2013-06-12 | 北京邮电大学 | Vision positioning method in dynamic environment |
| WO2017131334A1 (en) * | 2016-01-25 | 2017-08-03 | 충북대학교 산학협력단 | System and method for recognizing location of mobile robot and making map |
| CN105953798A (en) * | 2016-04-19 | 2016-09-21 | 深圳市神州云海智能科技有限公司 | Determination method and apparatus for poses of mobile robot |
| CN106052676A (en) * | 2016-05-26 | 2016-10-26 | 深圳市神州云海智能科技有限公司 | Robot navigation positioning method and device and robot |
| CN106197427A (en) * | 2016-07-04 | 2016-12-07 | 上海思依暄机器人科技股份有限公司 | Method, device and the robot of a kind of indoor positioning navigation |
| CN106525053A (en) * | 2016-12-28 | 2017-03-22 | 清研华宇智能机器人（天津）有限责任公司 | Indoor positioning method for mobile robot based on multi-sensor fusion |
Non-Patent Citations (4)
| Title |
|---|
| MIGUEL MARTÍNEZ-REY 等: "Smart laser scanner for event-based state estimation applied to indoor positioning", 《2016 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN)》 * |
| YU-CHEOL LEE 等: "Adaptive localization for mobile robots in urban environments using low-cost sensors and enhanced topological map", 《2011 15TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR)》 * |
| 房雅洪: "面向室内复杂环境的同步定位与地图创建系统研究", 《中国优秀硕士学位论文全文数据库》 * |
| 王勇: "移动机器人的定位能力估计及定位", 《中国博士学位论文全文数据库》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109489660A (en) * | 2018-10-09 | 2019-03-19 | 上海岚豹智能科技有限公司 | Robot localization method and apparatus |
| WO2020211655A1 (en) * | 2019-04-17 | 2020-10-22 | 北京迈格威科技有限公司 | Laser coarse registration method, device, mobile terminal and storage medium |
| WO2021000809A1 (en) * | 2019-07-03 | 2021-01-07 | 深圳市杉川机器人有限公司 | Method, apparatus, and system for constructing map in long corridor by using laser slam, and storage medium |
| CN112119326A (en) * | 2019-07-31 | 2020-12-22 | 深圳市大疆创新科技有限公司 | Data correction method, mobile platform and nonvolatile computer readable storage medium |
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| CN112987010A (en) * | 2021-01-28 | 2021-06-18 | 上海思岚科技有限公司 | System and method for multi-radar mapping of robot |
| CN113172658A (en) * | 2021-04-09 | 2021-07-27 | 北京猎户星空科技有限公司 | Robot positioning method, device, equipment and medium |
| CN113671523A (en) * | 2021-08-18 | 2021-11-19 | Oppo广东移动通信有限公司 | Robot positioning method, device, storage medium and robot |
| CN113899373A (en) * | 2021-09-30 | 2022-01-07 | 广州文远知行科技有限公司 | Initialization positioning method, device, vehicle and storage medium |
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| CN113984064A (en) * | 2021-10-27 | 2022-01-28 | 山东亚历山大智能科技有限公司 | Positioning method and system for indoor mobile robot |
| CN113984064B (en) * | 2021-10-27 | 2024-10-08 | 山东亚历山大智能科技有限公司 | Positioning method and system for indoor mobile robot |
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